Add complete workflow for using TA-Lib to bootstrap training data:
- generate_talib_annotations.py: Python script to run TA-Lib CDL* functions
and output span annotations in UI-compatible format
- import_talib_annotations.ts: TypeScript script to import generated
annotations into the UI database with auto-label-type creation
- npm script 'import-annotations' for easy execution
- TALIB_WORKFLOW.md: Comprehensive guide covering the full cycle:
* Generate patterns with TA-Lib
* Import into UI
* Review and edit in browser
* Export and train model
* Compare predictions with TA-Lib detections
* Iterate for improvement
This enables the intended workflow: use TA-Lib for initial annotations,
manually refine them, then train a model that learns from corrections.
Complete step-by-step guide covering:
- Creating training data through annotations
- Exporting annotations and candle data
- Running the full ML pipeline
- Verifying model creation and loading
- Getting predictions in the UI
- Troubleshooting common issues
- Iteration through active learning loop
- Add disagreement detection logic comparing human annotations vs predictions
- Display prediction summary in PredictionPanel (agreements/disagreements)
- Wire up 'Show only disagreements' filter toggle
- Add loading overlay during prediction fetching
- Update docker-compose.yml with healthchecks for all services
- Update DEPLOYMENT.md with comprehensive ML service setup instructions
- Update README.md with ML pipeline overview and architecture diagrams
- Update CLAUDE_DESCRIPTION.md with v3.0.0 ML integration details
Remaining tasks (11.2, 11.4, 11.5) deferred - core functionality complete
- Add histogram series to CandleChart for per-bar prediction colors (15% opacity)
- Add series markers showing label name and confidence % at prediction span starts
- Implement confidence threshold filtering for both histogram and markers
- Implement label type filtering from PredictionPanel checkboxes
- Implement prediction layer visibility toggle (show/hide)
- Add getVisibleCandles method to CandleChartHandle for on-demand prediction fetching
- Pass prediction state props from page.tsx to CandleChart
Tasks 10.1-10.5 complete.
- Create prediction type definitions in src/types/predictions.ts
- Add prediction state management to page.tsx with caching
- Implement PredictionPanel component with:
- Master visibility toggle
- Model info display (name, version, type, metrics)
- Action buttons (Run on Visible, Predict All)
- Confidence threshold slider
- Label filter checkboxes with per-class metrics
- Disagreement filter toggle
- Prediction summary display
- Model server offline banner
- Add on-demand and batch prediction fetching
- Implement prediction caching by chart and model version
- Add health polling for inference API (30s interval when offline)
- Ensure annotation tools work independently of prediction API
Tasks completed: 9.1-9.5, 12.1-12.3 (59/78 total)
- Add GET /api/span-annotations/export endpoint for ML pipeline JSON/CSV export
- Add source and model_prediction fields to span_annotations schema
- Update POST endpoint to accept source (human/model/human_correction) and model_prediction metadata
- Support negative annotations (label 'O' for user corrections to model predictions)
- Create migration 0005 for new schema fields
Completes tasks 8.1-8.4 of candle-backend change
- Create RandomForestModel and XGBoostModel wrappers with class weight support
- Implement temporal and random train/val/test splitting
- Add MLflow experiment tracking with full parameter and metric logging
- Create evaluation module for confusion matrix, feature importance, and classification reports
- Implement model training with sklearn/xgboost flavor logging and optional registry registration
- Store training run metadata in PostgreSQL
- Wire training stage into pipeline.py orchestrator
- Support both RandomForest and XGBoost models with configurable hyperparameters
- Add SpanAnnotation and SpanLabelType interfaces to page.tsx
- Add span-related state: spanAnnotations, selectedSpanId, spanLabelTypes
- Add fetchSpanAnnotations() and fetchSpanLabelTypes() data fetching functions
- Load span annotations and label types when chart changes
- Add "Span" tool button to Toolbox component with RectangleHorizontal icon
- Mark Section 5 tasks (5.1-5.5) as complete in tasks.md
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Created SpanRectanglePrimitive.ts implementing ISeriesPrimitive interface
- Implemented updateAllViews() with coordinate conversion from data-space to pixel-space
- Added semi-transparent rectangle rendering with configurable opacity
- Implemented label tag rendering above rectangles showing pattern names
- Added hitTest() for click detection within span bounds
- Implemented highlight state with thicker borders and increased opacity
- Set zOrder to 'bottom' to render rectangles behind candlesticks
- Completed Section 4 tasks (4.1-4.6) from span-annotation specification
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add span_label_types and span_annotations tables to schema
- Seed default span label types (bull_flag, bear_flag, etc.)
- Implement CRUD API endpoints for span label types
- Implement CRUD API endpoints for span annotations
- Add time swap validation in POST endpoint (start_time <= end_time)
- Verified ChartSelector uses theme-aware classes
- Tested full workflow: upload, chart creation, switching, annotation scoping
- Verified migration creates default chart with existing data
- Tested chart deletion with cascade delete of candles/annotations
- Tested edge cases: duplicate filenames, deleting last chart, upload with no charts
- Build passes with no type errors
- Add activeChartId/charts state to page.tsx with chart fetching on mount
- ChartSelector integrated in sidebar between header and file upload
- CandleChart and SvgOverlay fetch data scoped by activeChartId
- FileUpload returns chart info, auto-selects new chart after upload
- Annotation create/delete flows include chart_id
- Export scoped by activeChartId
- Toolbox receives activeChartId prop
- GET /api/candles accepts ?chartId= with fallback to most recent chart
- GET /api/annotations accepts ?chartId= with fallback to most recent chart
- POST /api/annotations now requires chart_id in request body
- GET /api/export accepts ?chartId= to scope exported annotations
- DELETE /api/annotations supports optional chartId scoping
- POST /api/upload now creates a chart named from the CSV filename
- Duplicate names get numeric suffix (e.g., btc-daily-2)
- Candles inserted with chart_id instead of replacing all data
- Response includes chart id and name
- Add charts table with id, name (unique), created_at
- Add chart_id FK to candles table with composite unique on (chart_id, time)
- Add chart_id FK to annotations table
- Custom migration handles existing data: creates 'Imported Data' chart and backfills chart_id
- Recreates tables for NOT NULL constraint (SQLite limitation)
- Change bullish candles to white interior with black outline
- Change bearish candles to solid black
- Update all wick colors to black
- Enable borders for candlesticks
